{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "given-exhaust",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.362271Z",
     "iopub.status.busy": "2021-03-18T08:36:38.361708Z",
     "iopub.status.idle": "2021-03-18T08:36:38.365627Z",
     "shell.execute_reply": "2021-03-18T08:36:38.365103Z"
    },
    "papermill": {
     "duration": 0.057215,
     "end_time": "2021-03-18T08:36:38.365764",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.308549",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR10\" # CIFAR10, CIFAR100, SVHN\n",
    "v_type = \"mingd\" # mingd, rand\n",
    "root_path = \"\"\n",
    "params_path = \"\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "severe-brooklyn",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.450794Z",
     "iopub.status.busy": "2021-03-18T08:36:38.450236Z",
     "iopub.status.idle": "2021-03-18T08:36:38.454169Z",
     "shell.execute_reply": "2021-03-18T08:36:38.453468Z"
    },
    "papermill": {
     "duration": 0.046844,
     "end_time": "2021-03-18T08:36:38.454319",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.407475",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR10\"\n",
    "v_type = \"rand\"\n",
    "root_path = \"/home/mohammad/transferability/files\"\n",
    "params_path = \"/home/mohammad/transferability/src\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "solar-chess",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.548744Z",
     "iopub.status.busy": "2021-03-18T08:36:38.538303Z",
     "iopub.status.idle": "2021-03-18T08:36:38.552906Z",
     "shell.execute_reply": "2021-03-18T08:36:38.554030Z"
    },
    "papermill": {
     "duration": 0.058239,
     "end_time": "2021-03-18T08:36:38.554320",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.496081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# CIFAR10 - Blind Walk results"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown\n",
    "v_type2disp = dict(mingd=\"MinGD\", rand=\"Blind Walk\")\n",
    "display(Markdown(f\"# {dataset} - {v_type2disp[v_type]} results\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "inner-settle",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.640573Z",
     "iopub.status.busy": "2021-03-18T08:36:38.640022Z",
     "iopub.status.idle": "2021-03-18T08:36:39.138360Z",
     "shell.execute_reply": "2021-03-18T08:36:39.139277Z"
    },
    "papermill": {
     "duration": 0.543549,
     "end_time": "2021-03-18T08:36:39.139586",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.596037",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.insert(0, params_path)\n",
    "\n",
    "import argparse, params, time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from importlib import reload\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import random\n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "naked-joining",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:39.211483Z",
     "iopub.status.busy": "2021-03-18T08:36:39.211058Z",
     "iopub.status.idle": "2021-03-18T08:36:39.867145Z",
     "shell.execute_reply": "2021-03-18T08:36:39.866102Z"
    },
    "papermill": {
     "duration": 0.689549,
     "end_time": "2021-03-18T08:36:39.867393",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.177844",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "vulnerable-niger",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:39.940069Z",
     "iopub.status.busy": "2021-03-18T08:36:39.939438Z",
     "iopub.status.idle": "2021-03-18T08:36:39.942683Z",
     "shell.execute_reply": "2021-03-18T08:36:39.942061Z"
    },
    "papermill": {
     "duration": 0.03923,
     "end_time": "2021-03-18T08:36:39.942823",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.903593",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import combine_pvalues, ttest_ind_from_stats, ttest_ind\n",
    "from functools import reduce\n",
    "from scipy.stats import hmean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-threshold",
   "metadata": {
    "papermill": {
     "duration": 0.029782,
     "end_time": "2021-03-18T08:36:40.008382",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.978600",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Use the full vectors to train a linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "located-wages",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.073371Z",
     "iopub.status.busy": "2021-03-18T08:36:40.072914Z",
     "iopub.status.idle": "2021-03-18T08:36:40.075401Z",
     "shell.execute_reply": "2021-03-18T08:36:40.075739Z"
    },
    "papermill": {
     "duration": 0.038312,
     "end_time": "2021-03-18T08:36:40.075861",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.037549",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "root = os.path.join(root_path,dataset)\n",
    "model_types = ['zero-shot', 'prune', 'fine-tune', 'extract-label', 'extract-logit', 'distillation', 'teacher']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tamil-monte",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.149945Z",
     "iopub.status.busy": "2021-03-18T08:36:40.149503Z",
     "iopub.status.idle": "2021-03-18T08:36:40.162711Z",
     "shell.execute_reply": "2021-03-18T08:36:40.161767Z"
    },
    "papermill": {
     "duration": 0.051905,
     "end_time": "2021-03-18T08:36:40.162938",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.111033",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\" and dataset == \"CIFAR100\": v_type = \"topgd\"\n",
    "    \n",
    "names = [\"teacher\",\"distillation\",\"extract-label\",\"extract-logit\",\"zero-shot\",\"fine-tune\",\"pre-act-18\",\"independent\"]\n",
    "trains = {}\n",
    "tests = {}\n",
    "for name in names:\n",
    "    trains[name] = (torch.load(f\"{root}/model_{name}_normalized/train_{v_type}_vulnerability.pt\"))\n",
    "    tests[name] = (torch.load(f\"{root}/model_{name}_normalized/test_{v_type}_vulnerability.pt\"))\n",
    "mean_cifar = trains[\"teacher\"].mean(dim = (0,1))\n",
    "std_cifar = trains[\"teacher\"].std(dim = (0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "russian-peoples",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.239887Z",
     "iopub.status.busy": "2021-03-18T08:36:40.239136Z",
     "iopub.status.idle": "2021-03-18T08:36:40.252933Z",
     "shell.execute_reply": "2021-03-18T08:36:40.254004Z"
    },
    "papermill": {
     "duration": 0.053933,
     "end_time": "2021-03-18T08:36:40.254393",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.200460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\":\n",
    "    for name in names:\n",
    "        trains[name] = trains[name].sort(dim = 1)[0]\n",
    "        tests[name] = tests[name].sort(dim = 1)[0]\n",
    "        \n",
    "for name in names:\n",
    "    trains[name] = (trains[name]- mean_cifar)/std_cifar\n",
    "    tests[name] = (tests[name]- mean_cifar)/std_cifar\n",
    "    \n",
    "f_num = 30\n",
    "a_num=30\n",
    "\n",
    "trains_n = {}\n",
    "tests_n = {}\n",
    "for name in names:\n",
    "    trains_n[name] = trains[name].T.reshape(1000,f_num)[:,:a_num]\n",
    "    tests_n[name] = tests[name].T.reshape(1000,f_num)[:,:a_num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "separate-technique",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.328150Z",
     "iopub.status.busy": "2021-03-18T08:36:40.327699Z",
     "iopub.status.idle": "2021-03-18T08:36:40.332106Z",
     "shell.execute_reply": "2021-03-18T08:36:40.331713Z"
    },
    "papermill": {
     "duration": 0.039788,
     "end_time": "2021-03-18T08:36:40.332210",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.292422",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_ex = split_index\n",
    "train = torch.cat((trains_n[\"teacher\"][:n_ex], tests_n[\"teacher\"][:n_ex]), dim = 0)\n",
    "y = torch.cat((torch.zeros(n_ex), torch.ones(n_ex)), dim = 0)\n",
    "\n",
    "rand=torch.randperm(y.shape[0])\n",
    "train = train[rand]\n",
    "y = y[rand]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "european-elements",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.404733Z",
     "iopub.status.busy": "2021-03-18T08:36:40.404275Z",
     "iopub.status.idle": "2021-03-18T08:36:40.408538Z",
     "shell.execute_reply": "2021-03-18T08:36:40.409113Z"
    },
    "papermill": {
     "duration": 0.041366,
     "end_time": "2021-03-18T08:36:40.409292",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.367926",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = nn.Sequential(nn.Linear(a_num,100),nn.ReLU(),nn.Linear(100,1),nn.Tanh())\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer =torch.optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "funded-wilson",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.483572Z",
     "iopub.status.busy": "2021-03-18T08:36:40.483118Z",
     "iopub.status.idle": "2021-03-18T08:36:43.977572Z",
     "shell.execute_reply": "2021-03-18T08:36:43.976923Z"
    },
    "papermill": {
     "duration": 3.531335,
     "end_time": "2021-03-18T08:36:43.977757",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.446422",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6f7522ff6dd4a12b875da6c83411425",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tqdm(range(1000)) as pbar:\n",
    "    for epoch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        inputs = train\n",
    "        outputs = model(inputs)\n",
    "        loss = -1 * ((2*y-1)*(outputs.squeeze(-1))).mean()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        pbar.set_description('loss {}'.format(loss.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-hindu",
   "metadata": {
    "papermill": {
     "duration": 0.080487,
     "end_time": "2021-03-18T08:36:44.094721",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.014234",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "framed-invitation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.234002Z",
     "iopub.status.busy": "2021-03-18T08:36:44.233233Z",
     "iopub.status.idle": "2021-03-18T08:36:44.237173Z",
     "shell.execute_reply": "2021-03-18T08:36:44.236192Z"
    },
    "papermill": {
     "duration": 0.074583,
     "end_time": "2021-03-18T08:36:44.237406",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.162823",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_p(outputs_train, outputs_test):\n",
    "    pred_test = outputs_test[:,0].detach().cpu().numpy()\n",
    "    pred_train = outputs_train[:,0].detach().cpu().numpy()\n",
    "    tval, pval = ttest_ind(pred_test, pred_train, alternative=\"greater\", equal_var=False)\n",
    "    if pval < 0:\n",
    "        raise Exception(f\"p-value={pval}\")\n",
    "    return pval\n",
    "\n",
    "def get_p_values(num_ex, train, test, k):\n",
    "    total = train.shape[0]\n",
    "    sum_p = 0\n",
    "    p_values = []\n",
    "    positions_list = []\n",
    "    for i in range(k):\n",
    "        positions = torch.randperm(total)[:num_ex]\n",
    "        p_val = get_p(train[positions], test[positions])\n",
    "        positions_list.append(positions)\n",
    "        p_values.append(p_val)\n",
    "    return p_values\n",
    "\n",
    "def get_fischer(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return combine_pvalues(p_values, method=\"mudholkar_george\")[1]\n",
    "\n",
    "def get_max_p_value(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return max(p_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "twenty-stations",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.353498Z",
     "iopub.status.busy": "2021-03-18T08:36:44.352170Z",
     "iopub.status.idle": "2021-03-18T08:36:44.361968Z",
     "shell.execute_reply": "2021-03-18T08:36:44.362455Z"
    },
    "papermill": {
     "duration": 0.066918,
     "end_time": "2021-03-18T08:36:44.362627",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.295709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "outputs_tr = {}\n",
    "outputs_te = {}\n",
    "for name in names:\n",
    "    outputs_tr[name] = model(trains_n[name])\n",
    "    outputs_te[name] = model(tests_n[name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "pretty-occurrence",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.470088Z",
     "iopub.status.busy": "2021-03-18T08:36:44.469511Z",
     "iopub.status.idle": "2021-03-18T08:36:44.471365Z",
     "shell.execute_reply": "2021-03-18T08:36:44.471831Z"
    },
    "papermill": {
     "duration": 0.059131,
     "end_time": "2021-03-18T08:36:44.471980",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.412849",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "def print_inference(outputs_train, outputs_test):\n",
    "    m1, m2 = outputs_test[:,0].mean(), outputs_train[:,0].mean()\n",
    "    pval = get_p(outputs_train, outputs_test)\n",
    "    print(f\"p-value = {pval} \\t| Mean difference = {m1-m2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "australian-manitoba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.576276Z",
     "iopub.status.busy": "2021-03-18T08:36:44.575563Z",
     "iopub.status.idle": "2021-03-18T08:36:44.578232Z",
     "shell.execute_reply": "2021-03-18T08:36:44.577611Z"
    },
    "papermill": {
     "duration": 0.06996,
     "end_time": "2021-03-18T08:36:44.578384",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.508424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for name in names:\n",
    "    outputs_tr[name], outputs_te[name] = outputs_tr[name][split_index:], outputs_te[name][split_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "failing-helena",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.672016Z",
     "iopub.status.busy": "2021-03-18T08:36:44.671565Z",
     "iopub.status.idle": "2021-03-18T08:36:44.686372Z",
     "shell.execute_reply": "2021-03-18T08:36:44.685334Z"
    },
    "papermill": {
     "duration": 0.051181,
     "end_time": "2021-03-18T08:36:44.686598",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.635417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teacher\n",
      "p-value = 0.0 \t| Mean difference = 1.8276243209838867\n",
      "distillation\n",
      "p-value = 3.23475079014282e-64 \t| Mean difference = 0.8124659657478333\n",
      "extract-label\n",
      "p-value = 2.2190777114361208e-98 \t| Mean difference = 1.02196204662323\n",
      "extract-logit\n",
      "p-value = 2.925545712707288e-103 \t| Mean difference = 1.060593605041504\n",
      "zero-shot\n",
      "p-value = 4.6888223586326287e-29 \t| Mean difference = 0.3938170075416565\n",
      "fine-tune\n",
      "p-value = 0.0 \t| Mean difference = 1.8371338844299316\n",
      "pre-act-18\n",
      "p-value = 6.740651462127559e-186 \t| Mean difference = 1.3808178901672363\n",
      "independent\n",
      "p-value = 0.9999999999999925 \t| Mean difference = -0.42001965641975403\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print(f\"{name}\")\n",
    "    print_inference(outputs_tr[name], outputs_te[name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blond-payment",
   "metadata": {
    "papermill": {
     "duration": 0.031666,
     "end_time": "2021-03-18T08:36:44.758214",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.726548",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Main Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "silver-yahoo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.840854Z",
     "iopub.status.busy": "2021-03-18T08:36:44.839595Z",
     "iopub.status.idle": "2021-03-18T08:36:44.950200Z",
     "shell.execute_reply": "2021-03-18T08:36:44.948969Z"
    },
    "papermill": {
     "duration": 0.1594,
     "end_time": "2021-03-18T08:36:44.950451",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.791051",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(f\"{dataset}_{v_type}.h5\"):\n",
    "    total_reps = 40\n",
    "    max_m = 45\n",
    "    total_inner_rep = 100\n",
    "\n",
    "    m_list = [x for x in range(2, max_m, 1)]\n",
    "    p_values_all_threat_models_dict = {}\n",
    "\n",
    "    n_pbar = tqdm(names, leave=False)\n",
    "    for name in n_pbar:\n",
    "        p_vals_per_rep_no = {}\n",
    "        r_pbar = tqdm(range(total_reps), leave=False)\n",
    "        for rep_no in r_pbar:\n",
    "            p_values_list = []\n",
    "            for m in m_list:\n",
    "                p_list = get_p_values(m, outputs_tr[name], outputs_te[name], total_inner_rep)\n",
    "                try:\n",
    "                    hm = hmean(p_list)\n",
    "                except:\n",
    "                    hm = 1.0\n",
    "                p_values_list.append(hm)\n",
    "                pbar.set_description(f\"{rep_no: 2d} {name} m={m: 3d}: f{hm: 0.5f}\")\n",
    "            r_pbar.set_description(f\"rep_no: {rep_no+1}/{total_reps}\")\n",
    "            p_vals_per_rep_no[rep_no] = p_values_list\n",
    "        p_values_all_threat_models_dict[name] = p_vals_per_rep_no\n",
    "        \n",
    "    df_list = []\n",
    "    for name, rep_dict in p_values_all_threat_models_dict.items():\n",
    "        df = pd.DataFrame(rep_dict).reset_index().assign(m=lambda r: r.index+2).drop([\"index\"], axis=1)\n",
    "        df_list.append(pd.melt(df,id_vars=[\"m\"], var_name=\"rep_no\", value_name=\"p_value\").assign(threat_model=name))\n",
    "    results_df = pd.concat(df_list)\n",
    "    \n",
    "    results_df.to_hdf(f\"{dataset}_{v_type}.h5\", v_type)\n",
    "    results_df\n",
    "else:\n",
    "    results_df = pd.read_hdf(f\"{dataset}_{v_type}.h5\", v_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-hamburg",
   "metadata": {
    "papermill": {
     "duration": 0.032992,
     "end_time": "2021-03-18T08:36:45.023110",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.990118",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "previous-clearing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.090530Z",
     "iopub.status.busy": "2021-03-18T08:36:45.090086Z",
     "iopub.status.idle": "2021-03-18T08:36:45.092885Z",
     "shell.execute_reply": "2021-03-18T08:36:45.092503Z"
    },
    "papermill": {
     "duration": 0.038517,
     "end_time": "2021-03-18T08:36:45.092991",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.054474",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams['text.latex.preamble'] = r'\\usepackage{times}'\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forbidden-attitude",
   "metadata": {
    "papermill": {
     "duration": 0.031412,
     "end_time": "2021-03-18T08:36:45.160359",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.128947",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "engaging-theory",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.230855Z",
     "iopub.status.busy": "2021-03-18T08:36:45.230202Z",
     "iopub.status.idle": "2021-03-18T08:36:45.234004Z",
     "shell.execute_reply": "2021-03-18T08:36:45.233355Z"
    },
    "papermill": {
     "duration": 0.0421,
     "end_time": "2021-03-18T08:36:45.234141",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.192041",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rename_dict = {\n",
    "    \"threat_model\": \"Threat Model\", \n",
    "    \"teacher\": \"Source\", \n",
    "    \"distillation\": \"Distillation\", \n",
    "    \"extract-label\": \"Label-Query\", \n",
    "    \"extract-logit\": \"Logit-Query\", \n",
    "    \"zero-shot\": \"Zero-Shot Learning\",\n",
    "    \"fine-tune\": \"Fine-Tuning\",\n",
    "    \"pre-act-18\": \"Diff. Architecture\",\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "junior-being",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.317532Z",
     "iopub.status.busy": "2021-03-18T08:36:45.315556Z",
     "iopub.status.idle": "2021-03-18T08:36:45.347877Z",
     "shell.execute_reply": "2021-03-18T08:36:45.346973Z"
    },
    "papermill": {
     "duration": 0.069018,
     "end_time": "2021-03-18T08:36:45.348103",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.279085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_df = (results_df.rename(columns=rename_dict).replace(rename_dict)\n",
    "           .query('(m >= 5)')\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "hourly-cloud",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.426929Z",
     "iopub.status.busy": "2021-03-18T08:36:45.426505Z",
     "iopub.status.idle": "2021-03-18T08:36:45.429293Z",
     "shell.execute_reply": "2021-03-18T08:36:45.428922Z"
    },
    "papermill": {
     "duration": 0.042398,
     "end_time": "2021-03-18T08:36:45.429396",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.386998",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Sizes\n",
    "SMALL_SIZE = 14\n",
    "MEDIUM_SIZE = SMALL_SIZE + 1\n",
    "BIGGER_SIZE = MEDIUM_SIZE + 1\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsi ze of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dirty-vulnerability",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.522926Z",
     "iopub.status.busy": "2021-03-18T08:36:45.521769Z",
     "iopub.status.idle": "2021-03-18T08:36:52.050080Z",
     "shell.execute_reply": "2021-03-18T08:36:52.051013Z"
    },
    "papermill": {
     "duration": 6.584773,
     "end_time": "2021-03-18T08:36:52.051307",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.466534",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'p-value')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n",
    "g = sns.lineplot(x=\"m\", y=\"p_value\", hue=\"Threat Model\", data=plot_df, ax=ax, ci=99)\n",
    "g.axhline(0.05, ls='--', color=\"black\", alpha=0.4)\n",
    "g.axhline(0.01, ls='--', color=\"black\", alpha=0.4)\n",
    "g.set_xlabel(\"Number of Sample Revealed\")\n",
    "g.set_ylabel(\"p-value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amino-microphone",
   "metadata": {
    "papermill": {
     "duration": 0.043815,
     "end_time": "2021-03-18T08:36:52.146504",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.102689",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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      "               mean_diff  p_value\n",
      "teacher            1.823    0.000\n",
      "distillation       0.778    0.000\n",
      "pre-act-18         1.400    0.000\n",
      "zero-shot          0.406    0.010\n",
      "fine-tune          1.839    0.000\n",
      "extract-label      1.006    0.001\n",
      "extract-logit      1.048    0.000\n",
      "independent       -0.397    0.675\n"
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    "with pd.option_context(\"display.float_format\", lambda x: f\"{x: 0.3f}\"):\n",
    "    print(tab)"
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     "text": [
      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "{} & mean_diff &    p_value \\\\\n",
      "\\midrule\n",
      "teacher       &     1.823 & $10^{-42}$ \\\\\n",
      "distillation  &     0.778 & $10^{-05}$ \\\\\n",
      "pre-act-18    &     1.400 & $10^{-10}$ \\\\\n",
      "zero-shot     &     0.406 & $10^{-02}$ \\\\\n",
      "fine-tune     &     1.839 & $10^{-27}$ \\\\\n",
      "extract-label &     1.006 & $10^{-04}$ \\\\\n",
      "extract-logit &     1.048 & $10^{-04}$ \\\\\n",
      "independent   &    -0.397 & $10^{-01}$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
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